Species Tree Estimation Using ASTRAL: How Many Genes Are Enough?

Shekhar, Shubhanshu; Roch, Sébastien; Mirarab, Siavash

doi:10.1109/tcbb.2017.2757930

Cited by 34 publications

(55 citation statements)

References 56 publications

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“…On the other hand, when ILS is high enough, the only chance of estimating the species tree well is to have a large sample of gene trees; hence, unless the sample of genes is large enough to allow many genes to be retained after filtering, deleting genes will be detrimental. Therefore, the data quantity requirement depends not only on the method but also on the model condition, and most species tree methods probably require more data under high ILS model conditions for highly accurate species trees to be estimated (Shekhar et al 2017).…”

Section: Discussionmentioning

confidence: 99%

To include or not to include: The impact of gene filtering on species tree estimation methods

Molloy

Warnow

2017

Preprint

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Abstract.-Species tree estimation from loci sampled from multiple genomes is now common, but is challenged by the heterogeneity across the genome due to multiple processes, such as gene duplication and loss, horizontal gene transfer, and incomplete lineage sorting.Although methods for estimating species trees have been developed that address gene tree heterogeneity due to incomplete lineage sorting, many of these methods operate by combining estimated gene trees and are hence vulnerable to gene tree quality. There is also the added concern that missing data, which is frequently encountered in genome-scale datasets, will impact species tree estimation.. CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/149120 doi: bioRxiv preprint first posted online Jun. 12, 2017; Our study addresses the impact of gene filtering on species trees inferred from multigene datasets. We address these questions using a large and heterogeneous collection of simulated datasets both with and without missing data. We compare several established coalescent-based methods (ASTRAL, ASTRID, MP-EST, and SVDquartets within PAUP*) as well as unpartitioned concatenation using maximum likelihood (RAxML).Our study shows that gene tree error and missing data impact all methods (and some methods degrade more than others), but the degree of incomplete lineage sorting and gene tree estimation error impacts the absolute and relative performance of methods as well as their response to gene filtering strategies. We find that filtering genes based on the degree of missing data is either neutral or else reduces the accuracy of all five methods examined, and so is not recommended. Filtering genes based on gene tree estimation error shows somewhat different trends. Under low levels of incomplete lineage sorting, removing genes with high gene tree estimation error can improve the accuracy of summary methods, but only if not too many genes are removed. Otherwise, filtering genes tends to increase error, especially under high levels of incomplete lineage sorting. Hence, while filtering genes based on missing data is not recommended, there are conditions under which removing high error gene trees can improve species tree estimation. This study provides insights into prior studies and suggests approaches for analyzing phylogenomic datasets.(Keywords: species tree estimation, multi-species coalescent, incomplete lineage sorting, gene tree estimation error, missing data) Species tree estimation is greatly enabled through the use of multiple loci, and increased access to genomic data over the last decade has opened up the possibility of greatly improving our understanding of how life has evolved on earth (Posada 2016). Furthermore, . CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/149120 doi: bioR...

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Section: Discussionmentioning

confidence: 99%

To include or not to include: The impact of gene filtering on species tree estimation methods

Molloy

Warnow

2017

Preprint

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“…Consequently, effective gene filtering that improves species tree estimation requires finding a balance between data quantity versus data quality (Molloy and Warnow 2018). For low ILS, a few highly accurate gene trees are sufficient to estimate the true species tree (Shekhar et al 2017). This is evident, for example, in the concatenated analyses, which do not consider gene tree heterogeneity and hence do not account for ILS, and did not show changes in support or topology after filtering despite lower number of loci or taxa.…”

Section: Performance Of Filtering Strategiesmentioning

confidence: 99%

Resolving Deep Nodes in an Ancient Radiation of Neotropical Fishes in the Presence of Conflicting Signals from Incomplete Lineage Sorting

et al. 2018

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Resolving patterns of ancient and rapid diversifications is one of the most challenging tasks in evolutionary biology. These difficulties arise from confusing phylogenetic signals that are associated with the interplay of incomplete lineage sorting and homoplasy. Phylogenomic analyses of hundreds, or even thousands, of loci offer the potential to resolve such contentious relationships. Yet, how much useful phylogenetic information these large data sets contain remains uncertain and often goes untested. Here, we assess the utility of different data filtering approaches to maximize phylogenetic information and minimize noise when reconstructing an ancient radiation of Neotropical electric knifefishes (Order Gymnotiformes) using ultraconserved elements. We found two contrasting hypotheses of gymnotiform evolutionary relationships depending on whether phylogenetic inferences were based on concatenation or coalescent methods. In the first case, all analyses inferred a previously-and commonly-proposed hypothesis, where the family Apteronotidae was found as the sister group to all other gymnotiform families. In contrast, coalescent-based analyses suggested a novel hypothesis where families producing pulse-type (viz., Gymnotidae, Hypopomidae and Rhamphichthyidae) and wave-type electric signals (viz., Apteronotidae, Sternopygidae) were reciprocally monophyletic.Nodal support for this second hypothesis increased when analyzing loci with the highest phylogenetic information content and further increased when data were pruned using targeted filtering methods that maximized phylogenetic informativeness at the deepest nodes of the Gymnotiformes. Bayesian concordance analyses and topology tests of individual gene genealogies demonstrated that the difficulty of resolving this radiation was likely due to high gene-tree incongruences that resulted from incomplete lineage sorting. We show that data filtering reduces gene tree heterogeneity and increases nodal support and consistency of species Downloaded from https://academic.oup.com/sysbio/advance-article-abstract/doi/10.1093/sysbio/syy085/5232148 by Bukkyo University user on 06 December 20183 trees using coalescent methods; however, we failed to observe the same effect when using concatenation methods. Furthermore, the targeted filtering strategies applied here support the use of "gene data interrogation" rather than "gene genealogy interrogation" approaches in phylogenomic analyses, to extract phylogenetic signal from intractable portions of the Tree of Life.

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“…Many methods are available for combining gene trees (e.g., Kubatko et al, 2009;Liu et al, 2009;Mossel and Roch, 2010;Liu et al, 2010;Chaudhary et al, 2010;Liu and Yu, 2011;Wu, 2012;Bayzid et al, 2013;Sayyari and Mirarab, 2016a), and many of them are statistically consistent under various models of genome evolution. In particular, many of the summary methods have established statistical guarantees (Liu et al, 2010;Allman et al, 2016;Shekhar et al, 2017) under the multi-species coalescent model (Pamilo and Nei, 1988;Rannala and Yang, 2003), which can generate incomplete lineage sorting (ILS) (Degnan and Rosenberg, 2009). Several summary methods, including ASTRAL (Mirarab et al, 2014b), NJst/ASTRID (Liu and Yu, 2011;Vachaspati and Warnow, 2015), and MP-EST (Liu et al, 2010) are in wide use.…”

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confidence: 99%

INSTRAL: Discordance-aware Phylogenetic Placement using Quartet Scores

Rabiee

Mirarab

2018

Preprint

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Phylogenomic analyses have increasingly adopted species tree reconstruction using methods that account for gene tree discordance using pipelines that require both human effort and computational resources. As the number of available genomes continues to increase, a new problem is facing researchers. Once more species become available, they have to repeat the whole process from the beginning because updating species trees is currently not possible. However, the de novo inference can be prohibitively costly in human effort or machine time.In this paper, we introduce INSTRAL, a method that extends ASTRAL to enable phylogenetic placement. INSTRAL is designed to place a new species on an existing species tree after sequences from the new species have already been added to gene trees; thus, INSTRAL is complementary to existing placement methods that update gene trees.

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Species Tree Estimation Using ASTRAL: How Many Genes Are Enough?

Cited by 34 publications

References 56 publications

To include or not to include: The impact of gene filtering on species tree estimation methods

To include or not to include: The impact of gene filtering on species tree estimation methods

Resolving Deep Nodes in an Ancient Radiation of Neotropical Fishes in the Presence of Conflicting Signals from Incomplete Lineage Sorting

INSTRAL: Discordance-aware Phylogenetic Placement using Quartet Scores

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